**NOTE: THIS DOCUMENT IS OBSOLETE, PLEASE CHECK THE NEW
VERSION:** "Mathematics of the Discrete
Fourier Transform (DFT), with Audio Applications --- Second
Edition", by Julius
O. Smith III, W3K
Publishing, 2007, ISBN 978-0-9745607-4-8. - Copyright ©
*2017-09-28* by Julius O. Smith III -
Center for Computer Research
in Music and Acoustics (CCRMA), Stanford University

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## Appendix: Matlab Examples

Here's how Fig. 6.1 was generated in Matlab:

>> x = [2 3]; % coordinates of x >> origin = [0 0]; % coordinates of the origin >> xcoords = [origin(1) x(1)]; % plot() expects coordinate lists, not endpoints >> ycoords = [origin(2) x(2)]; >> plot(xcoords,ycoords); % Draw a line from origin to xMathematica can plot a list of ordered pairs:In[1]: ListPlot[{{0,0},{2,3}},PlotJoined->True]; (* Draw a line from (0,0) to (2,3) *)In Matlab, the

meanof the row-vector can be computed as

or by using the built-in functionmean().In Matlab, if

x = [x1 ... xN]is a row vector, we can compute thetotal energyas

Matlab has a function

orth()which will compute an orthonormal basis for a space given any set of vectors which span the space.>> help orth

ORTH Orthogonalization. Q = orth(A) is an orthonormal basis for the range of A. Q’*Q = I, the columns of Q span the same space as the columns of A and the number of columns of Q is the rank of A.`See also QR, NULL.</pre>Below is an example of using <tt>orth()</tt> to orthonormalize a <a href="http://mathworld.wolfram.com/linearlyindependent.php">linearly`

independent basis set for :% Demonstration of the Matlab function orth() for % taking a set of vectors and returning an orthonormal set % which span the same space. v1 = [1; 2; 3]; % our first basis vector (a column vector) v2 = [1; -2; 3]; % a second, linearly independent column vector v1’ * v2 % show that v1 is not orthogonal to v2

ans =<< Previous page TOC INDEX Next page >>`6`

V = [v1,v2] % Each column of V is one of our vectors

V =`1 1 2 -2 3 3`

W = orth(V) % Find an orthonormal basis for the same space

W =`0.2673 0.1690 0.5345 -0.8452 0.8018 0.5071`

w1 = W(:,1) % Break out the returned vectors

w1 =`0.2673 0.5345 0.8018`

w2 = W(:,2)

w2 =`0.1690`

-0.8452 0.5071

w1’ * w2 % Check that w1 is orthogonal to w2 (to working precision)

ans =

2.5723e-17

w1’ * w1 % Also check that the new vectors are unit length in 3D

ans =`1`

w2’ * w2

ans =`1`

W’ * W % faster way to do the above checks (matrix multiplication)

ans =`1.0000 0.0000 0.0000 1.0000`

% Construct some vector x in the space spanned by v1 and v2: x = 2 * v1 - 3 * v2

x =`-1 10 -3`

% Show that x is also some linear combination of w1 and w2: c1 = x’ * w1 % Coefficient of projection of x onto w1

c1 =`2.6726`

c2 = x’ * w2 % Coefficient of projection of x onto w2

c2 =

-10.1419

xw = c1 * w1 + c2 * w2 % Can we make x using w1 and w2?

xw =

-1.0000 10.0000 -3.0000

error = x - xw

error =

1.0e-14 *`0.1332 0 0<a href="https://ccrma.stanford.edu/~jos//interpolation/lp_norms.php">norm</a>(error) % typical way to summarize a vector error`

ans =

1.3323e-15

% It works!

% Now, construct some vector x NOT in the space spanned by v1 and v2: y = [1; 0; 0]; % Almost anything we guess in 3D will work

% Try to express y as a linear combination of w1 and w2: c1 = y’ * w1; % Coefficient of projection of y onto w1 c2 = y’ * w2; % Coefficient of projection of y onto w2 yw = c1 * w1 + c2 * w2 % Can we make y using w1 and w2?

yw =`0.1000 0.0000 0.3000`

yerror = y - yw

yerror =`0.9000 0.0000`

-0.3000

norm(yerror)

ans =`0.9487`

% While the error is not zero, it is the smallest possible % error in the least squares sense. % That is, yw is the optimal least-squares approximation % to y in the space spanned by v1 and v2 (w1 and w2). % In other words, norm(yerror) <= norm(y-yw2) for any other vector yw2 made % using a linear combination of v1 and v2.

% In yet other words, we obtain the optimal least squares approximation % of y (which lives in 3D) in some subspace W (a 2D subspace of 3D) % by projecting y orthogonally onto the subspace W to get yw as above. % % An important property of the optimal least-squares approximation % is that the approximation error is orthogonal to the the subspace % in which the approximation lies. Let’s show this:

W’ * yerror % must be zero to working precision

ans =

1.0e-16 *

-0.2574 -0.0119